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CONCEPTUAL AND OPERATIONAL DEFINITIONS

Although most of the interesting variables in social science are concepts, some of our most important concepts are not variables. The concept of “positivism” is not a variable, but the concept of “philosophies of science’’ is a variable, and positivism is one member of the list of those philosophies. The concept of ‘‘love’’ is not a variable, but the concept of ‘‘being in love or not’’ is one. The concept of ‘‘culture’’ is not a variable, but the concept of‘‘intensity of feeling of belonging to a particular culture’’ is one. The concept of ‘‘attitude’’ is not a variable, but the concept of ‘‘attitude toward clitoridectomy as a violation of human rights’’ implies a variable with at least two attributes, support and nonsupport.

CONCEPTUAL DEFINITIONS

There are two ways to define variables—conceptually and operationally. Conceptual definitions are abstractions, articulated in words, that facilitate understanding. They are the sort of definitions we see in dictionaries, and we use them in everyday conversation to tell people what we mean by some term or phrase. Operational definitions consist of a set of instructions on how to measure a variable that has been conceptually defined.

Suppose I tell you that ‘‘Alice and Fred just moved to a spacious house.’’ Nice concept. You ask: ‘‘What do you mean by ‘spacious’?’’ and I say: ‘‘You know, big rooms, high ceilings.’’

If that isn’t enough for you, we’ll have to move from a conceptual definition of ‘‘spacious’’ to an operational one. We’ll have to agree on what to measure: Do we count the screened-in porch and the garage or just the interior living space? Do we count the square footage or the cubic footage? That is, do we get a measure of the living surface, or some measure of the ‘‘feeling of spaciousness’’ that comes from high ceilings? Do we measure the square footage of open space before or after the furniture and appliances go in? If we had to agree on things like this for every concept, ordinary human discourse would come to a grinding halt.

Science is not ordinary human discourse, however, and this, in my view, is the most important difference between the humanistic and the scientific (positivistic) approaches to social science. Humanistic researchers seek to maintain the essential feel of human discourse. Positivists focus more on specific measurement. I do not see these two styles as inimical to one another, but as complementary.

To get a feel for how complementary the two styles can be, ask some 50 year olds and some 20 year olds—men and women of both ages—to tell you how old you have to be in order to be middle aged. You’ll see immediately how volatile the conceptual definition of ‘‘middle age’’ is. If you ask people about what it means to ‘‘be middle aged,’’ you’ll get plenty of material for an interesting paper on the subject. If you want to measure the differences between men and women and between older and younger people on this variable, you’ll have to do more than just ask them. Figure 2.3 shows an instrument for measuring this variable.

FIGURE 2.3.

An instrument for measuring what people think ''middle age'' means.

Many concepts that we use in anthropology have volatile definitions: “power,” ‘‘social class,’’ ‘‘machismo,’’ ‘‘alienation,’’ “willingness to change,’’ and ‘‘fear of retribution.’’ If we are to talk sensibly about such things, we need clear, intersubjective definitions of them. In other words, although there can be no objective definition of middle age, we can at least agree on what we mean by ‘‘middle age’’ for a particular study and on how to measure the concept.

Complex variables are conceptually defined by reducing them to a series of simpler variables. Saying that ‘‘the people in this village are highly acculturated’’ can be interpreted in many ways. But if you state clearly that you include ‘‘being bilingual,’’ ‘‘working in the national economy,’’ and ‘‘going to school’’ in your conceptual definition of acculturation, then at least others will understand what you’re talking about when you say that people are ‘‘highly acculturated.’’

Similarly, ‘‘machismo’’ might be characterized by ‘‘a general feeling of male superiority,’’ accompanied by ‘‘insecure behavior in relationships with women.’’ Intelligence might be conceptually defined as ‘‘the ability to think in abstractions and to generalize from cases.’’ These definitions have something important in common: They have no external reality against which to test their truth value.

Conceptual definitions are at their most powerful when they are linked together to build theories that explain research results. When the United Nations was founded in 1945, the hope was that trade between industrialized and nonindustrialized countries of the world would result in economic development for everyone. The economies of the developed countries would expand and the benefits of an expanding economy would be seen in the underdeveloped countries. A decade later, it was obvious that this wasn’t what was happening. The rich countries were getting richer and the poor countries were getting poorer.

Raul Prebisch, an Argentinian economist who worked at the UN, argued that under colonialism, rich countries were importing raw materials from poor countries to produce manufactured goods and that poor countries had come to depend economically on the rich countries. Prebisch’s ‘‘dependency theory’’ links the concept of ‘‘control of capital’’ with those of ‘‘mutual security’’ and ‘‘economic dependency,’’ and the linkage helps explain why economic development often results in some groups winding up with less access to capital than they had before a development program (Prebisch 1984, 1994).

Conceptual definitions are at their weakest in the conduct of research itself, because concepts are abstractions—we have to make them up to study them.

There is nothing wrong with this. There are three things one wants to do in any science: (1) describe a phenomenon of interest; (2) explain what causes it; and (3) predict what it causes. The existence of a conceptual variable is inferred from what it predicts—how well it makes theoretical sense out of a lot of data.

 
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